Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90293
Título: | Energy efficiency of Python machine learning frameworks |
Autor(es): | Ajel, Salwa Ribeiro, Francisco Ejbali, Ridha Saraiva, João |
Palavras-chave: | DeepLearning Energy-Efficient Execution time Keras Machine Learning Memory usage Pytorch Tensorflow |
Data: | 2023 |
Editora: | Springer, Cham |
Revista: | Lecture Notes in Networks and Systems |
Citação: | Ajel, S., Ribeiro, F., Ejbali, R., Saraiva, J. (2023). Energy Efficiency of Python Machine Learning Frameworks. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_57 |
Resumo(s): | Although machine learning (ML) is a field that has been the subject of research for decades, a large number of applications with high computational power have recently emerged. Usually, we only focus on solving machine learning problems without considering how much energy has been consumed by the different frameworks used for such applications. This study aims to provide a comparison among four widely used frameworks such as Tensorflow, Keras, Pytorch, and Scikit-learn in terms of many aspects, including energy efficiency, memory usage, execution time, and accuracy. We monitor the performance of such frameworks using different well-known machine learning benchmark problems. Our results show interesting findings, such as slower and faster frameworks consuming less or more energy, higher or lower memory usage, etc. We show how to use our results to provide machine learning developers with information to decide which framework to use for their applications when energy efficiency is a concern. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/90293 |
ISBN: | 978-3-031-35506-6 |
e-ISBN: | 978-3-031-35507-3 |
DOI: | 10.1007/978-3-031-35507-3_57 |
ISSN: | 2367-3370 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-031-35507-3_57 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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isda2022-vol2.pdf | 604,8 kB | Adobe PDF | Ver/Abrir |